Software applications and systems have become indispensable tools for helping consumers, i.e., users, perform a wide variety of tasks in their daily professional and personal lives. Currently, numerous types of desktop, web-based, and cloud-based software systems are available to help users perform a plethora of tasks ranging from basic computing system operations and word processing, to financial management, small business management, tax preparation, health tracking and healthcare management, as well as other personal and business endeavors, operations, and functions far too numerous to individually delineate here.
One major, if not determinative, factor in the utility, and ultimate commercial success, of a given software system of any type is the ability to implement and provide a customer support system through which a given user can obtain assistance and, in particular, get answers to questions that arise during the installation and operation of the software system. However, providing potentially millions of software system users with specialized advice and answers to their specific questions is a huge undertaking that can easily, and rapidly, become economically infeasible.
To address this problem, many providers of software systems implement or sponsor customer support systems to complement their distributions of software system. However, traditional implementations of customer support systems fail to measure up to the expectations of many users. For some yet-to-be-discovered reason, many users expect the customer support system to understand what the user is thinking. That is, the user expects to be able to enter an extraordinarily short and poorly worded question or search query, while fully expecting the customer support system to correctly identify and/or accurately provide a response for the user. Truly, users appear to expect the customer support systems to act as mind readers.
Although science is quickly advancing, as far as the public is aware, mind reading is yet an impossible task. However, if the under-articulated, overly-concise, and/or poorly worded questions of users are met with unsatisfactory answers, the users communicate dissatisfaction by, for example, using competitors' software systems, providing negative reviews in forums, and/or avoiding other products that are available from the service provider.
What is needed is a method and system for personalizing a user experience in a customer support system by improving topic identification of search query terms in a customer support system, at least partially based on contextual information related to the search query terms, to improve the likelihood of customer satisfaction with the customer support system.